Clustering Indonesian Provinces Based on Welfare Level Using Several Validity Indices
DOI:
https://doi.org/10.12928/bamme.v6i1.15817Keywords:
clustering, Fuzzy C-Means , K-Means, level of welfare, validityAbstract
One of the national development goals is to increase the level of community welfare. There are several aspects that influence the level of welfare, namely population, health, education, housing, social, employment, consumption, and poverty. This research aims to group provinces in Indonesia based on their level of welfare so that the government can determine appropriate policies in the context of economic recovery and improving the welfare of the Indonesian people. The data used are indicators of provincial welfare levels in Indonesia in 2022 from the Central Statistics Agency. Data is grouped into 3 clusters based on welfare level, namely high (C1), medium (C2), and low (C3) using the K-Means and Fuzzy C-Means methods. Based on the results of the validity test, it is known that ththe best method is the K-Means method with Euclidean distance using the parameter k = 3, the resulting DBI value is 0.989 and the C-Index is 0.076, where this value is better than those of the Fuzzy C-Means method. It is hoped that the results can provide information regarding the characteristics of provinces in Indonesia based on welfare level indicators and become a reference for the government in improving welfare in Indonesia.
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